JOURNAL ARTICLE

Empirical Mode Decomposition Based Denoising By Customized Thresholding

Wahiba MohguenRaïs El’hadi Bekka

Year: 2017 Journal:   Zenodo (CERN European Organization for Nuclear Research) Vol: 11 (5)Pages: 404-409   Publisher: European Organization for Nuclear Research

Abstract

This paper presents a denoising method called EMD-Custom that was based on Empirical Mode Decomposition (EMD) and the modified Customized Thresholding Function (Custom) algorithms. EMD was applied to decompose adaptively a noisy signal into intrinsic mode functions (IMFs). Then, all the noisy IMFs got threshold by applying the presented thresholding function to suppress noise and to improve the signal to noise ratio (SNR). The method was tested on simulated data and real ECG signal, and the results were compared to the EMD-Based signal denoising methods using the soft and hard thresholding. The results showed the superior performance of the proposed EMD-Custom denoising over the traditional approach. The performances were evaluated in terms of SNR in dB, and Mean Square Error (MSE).

Keywords:
Thresholding Hilbert–Huang transform Noise reduction Decomposition Mode (computer interface) Artificial intelligence Computer science Pattern recognition (psychology) Computer vision Chemistry Human–computer interaction

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0.69
FWCI (Field Weighted Citation Impact)
0
Refs
0.74
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Citation History

Topics

Anomaly Detection Techniques and Applications
Physical Sciences →  Computer Science →  Artificial Intelligence
Machine Fault Diagnosis Techniques
Physical Sciences →  Engineering →  Control and Systems Engineering
Spectroscopy Techniques in Biomedical and Chemical Research
Life Sciences →  Biochemistry, Genetics and Molecular Biology →  Biophysics

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